The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{maheshwari:ipac2021-wepab318,
author = {M. Maheshwari and D.J. Dunning and J.K. Jones and M.P. King and H.R. Kockelbergh and A.E. Pollard},
title = {{Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm}},
booktitle = {Proc. IPAC'21},
pages = {3417--3420},
eid = {WEPAB318},
language = {english},
keywords = {FEL, network, simulation, electron, ECR},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB318},
url = {https://jacow.org/ipac2021/papers/wepab318.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318},
abstract = {{Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.}},
}